Why deployment delivers value in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the model's prediction on new data.

ML Python

prediction = model.[1](new_data)
print(prediction)

Drag options to blanks, or click blank then click option'

Atransform

Bpredict

Cfit

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' instead of 'predict' to get predictions.

The predict method is used to get the model's output on new data.

2fill in blank

medium

Complete the code to save the trained model for deployment.

ML Python

import joblib
joblib.[1](model, 'model.pkl')

Drag options to blanks, or click blank then click option'

Aload

Bsave

Cdump

Dexport

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'load' instead of 'dump' to save the model.

The dump function saves the model to a file for later use.

3fill in blank

hard

Fix the error in the code to deploy a model as a web service.

ML Python

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    prediction = model.[1](data['input'])
    return jsonify({'prediction': prediction.tolist()})

Drag options to blanks, or click blank then click option'

Afit

Bevaluate

Ctrain

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' or 'train' instead of 'predict' in deployment code.

Use predict to get the model's output on input data in deployment.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters features with importance above 0.1.

ML Python

important_features = {feature: importance for feature, importance in model.feature_importances_.items() if importance [1] 0.1 and feature [2] 'bias'}

Drag options to blanks, or click blank then click option'

A>

B<

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for importance check.

Using '==' instead of '!=' to exclude 'bias'.

We want features with importance greater than 0.1 and exclude the 'bias' feature.

5fill in blank

hard

Fill all three blanks to create a dictionary of predictions for inputs with confidence above 0.8.

ML Python

high_confidence_preds = [1]: [2] for [3] in inputs if model.predict_proba([[3]])[0][1] > 0.8}

Drag options to blanks, or click blank then click option'

Ainput

Bmodel.predict([input])[0]

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Using predict_proba output directly as prediction.

We use 'input' as the key and the model's prediction as the value for each input in inputs.